

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__snake_case				:      List[str]   		=			{
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 10_00,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__snake_case				:      Dict   		=			{
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 10_00,
    """block_out_channels""": [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__snake_case				:      Optional[Any]   		=			{
    """sample_size""": 2_56,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__snake_case				:      Union[str, Any]   		=			{
    """num_train_timesteps""": 40,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

__snake_case				:      Tuple   		=			{
    """num_train_timesteps""": 2_01,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

__snake_case				:      Any   		=			{
    """num_train_timesteps""": 1_51,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if isinstance(a__  ,	a__):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise argparse.ArgumentTypeError("""boolean value expected""")


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__=False):

			'''simple docstring'''



			a_						:						Dict 							= checkpoint[f'''{old_prefix}.in_layers.0.weight''']
			a_						:						Dict 							= checkpoint[f'''{old_prefix}.in_layers.0.bias''']
			a_						:						List[Any] 							= checkpoint[f'''{old_prefix}.in_layers.2.weight''']
			a_						:						List[str] 							= checkpoint[f'''{old_prefix}.in_layers.2.bias''']
			a_						:						Tuple 							= checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
			a_						:						int 							= checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
			a_						:						str 							= checkpoint[f'''{old_prefix}.out_layers.0.weight''']
			a_						:						Optional[int] 							= checkpoint[f'''{old_prefix}.out_layers.0.bias''']
			a_						:						Any 							= checkpoint[f'''{old_prefix}.out_layers.3.weight''']
			a_						:						Dict 							= checkpoint[f'''{old_prefix}.out_layers.3.bias''']

			if has_skip:
						a_						:						Optional[int] 							= checkpoint[f'''{old_prefix}.skip_connection.weight''']
						a_						:						Dict 							= checkpoint[f'''{old_prefix}.skip_connection.bias''']

			return new_checkpoint


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__=None):

			'''simple docstring'''



			a_							,					a_							,					a_						:						str 							= checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3  ,	dim=0)
			a_							,					a_							,					a_						:						Union[str, Any] 							= checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3  ,	dim=0)

			a_						:						List[str] 							= checkpoint[f'''{old_prefix}.norm.weight''']
			a_						:						Any 							= checkpoint[f'''{old_prefix}.norm.bias''']

			a_						:						List[Any] 							= weight_q.squeeze(-1).squeeze(-1)
			a_						:						Optional[Any] 							= bias_q.squeeze(-1).squeeze(-1)
			a_						:						List[Any] 							= weight_k.squeeze(-1).squeeze(-1)
			a_						:						List[str] 							= bias_k.squeeze(-1).squeeze(-1)
			a_						:						Tuple 							= weight_v.squeeze(-1).squeeze(-1)
			a_						:						List[Any] 							= bias_v.squeeze(-1).squeeze(-1)

			a_						:						str 							= (
			    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1).squeeze(-1)
			)
			a_						:						Optional[int] 							= checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1).squeeze(-1)

			return new_checkpoint


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						int 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						Optional[Any] 							= {}

			a_						:						Tuple 							= checkpoint["""time_embed.0.weight"""]
			a_						:						List[Any] 							= checkpoint["""time_embed.0.bias"""]
			a_						:						Optional[Any] 							= checkpoint["""time_embed.2.weight"""]
			a_						:						List[Any] 							= checkpoint["""time_embed.2.bias"""]

			if unet_config["num_class_embeds"] is not None:
						a_						:						Dict 							= checkpoint["""label_emb.weight"""]

			a_						:						Dict 							= checkpoint["""input_blocks.0.0.weight"""]
			a_						:						Optional[int] 							= checkpoint["""input_blocks.0.0.bias"""]

			a_						:						int 							= unet_config["""down_block_types"""]
			a_						:						Dict 							= unet_config["""layers_per_block"""]
			a_						:						Optional[Any] 							= unet_config["""attention_head_dim"""]
			a_						:						Any 							= unet_config["""block_out_channels"""]
			a_						:						int 							= 1
			a_						:						str 							= channels_list[0]

			for i, layer_type in enumerate(a__):
						a_						:						Optional[Any] 							= channels_list[i]
						a_						:						Any 							= current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(a__):
												a_						:						Optional[int] 							= f'''down_blocks.{i}.resnets.{j}'''
												a_						:						List[str] 							= f'''input_blocks.{current_layer}.0'''
												a_						:						List[Any] 							= True if j == 0 and downsample_block_has_skip else False
												a_						:						Optional[Any] 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__  ,	has_skip=a__)
												current_layer += 1

						elif layer_type == "AttnDownBlock2D":
									for j in range(a__):
												a_						:						List[str] 							= f'''down_blocks.{i}.resnets.{j}'''
												a_						:						Dict 							= f'''input_blocks.{current_layer}.0'''
												a_						:						int 							= True if j == 0 and downsample_block_has_skip else False
												a_						:						Any 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__  ,	has_skip=a__)
												a_						:						Optional[int] 							= f'''down_blocks.{i}.attentions.{j}'''
												a_						:						int 							= f'''input_blocks.{current_layer}.1'''
												a_						:						Dict 							= convert_attention(
												    a__  ,	a__  ,	a__  ,	a__  ,	a__)
												current_layer += 1

						if i != len(a__) - 1:
									a_						:						str 							= f'''down_blocks.{i}.downsamplers.0'''
									a_						:						Any 							= f'''input_blocks.{current_layer}.0'''
									a_						:						List[str] 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__)
									current_layer += 1

						a_						:						int 							= current_channels

			# hardcoded the mid-block for now
			a_						:						List[Any] 							= """mid_block.resnets.0"""
			a_						:						List[str] 							= """middle_block.0"""
			a_						:						Tuple 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__)
			a_						:						Dict 							= """mid_block.attentions.0"""
			a_						:						str 							= """middle_block.1"""
			a_						:						Union[str, Any] 							= convert_attention(a__  ,	a__  ,	a__  ,	a__  ,	a__)
			a_						:						Tuple 							= """mid_block.resnets.1"""
			a_						:						Dict 							= """middle_block.2"""
			a_						:						List[str] 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__)

			a_						:						Union[str, Any] 							= 0
			a_						:						Tuple 							= unet_config["""up_block_types"""]

			for i, layer_type in enumerate(a__):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1):
												a_						:						Optional[Any] 							= f'''up_blocks.{i}.resnets.{j}'''
												a_						:						List[Any] 							= f'''output_blocks.{current_layer}.0'''
												a_						:						List[Any] 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__  ,	has_skip=a__)
												current_layer += 1

									if i != len(a__) - 1:
												a_						:						Optional[Any] 							= f'''up_blocks.{i}.upsamplers.0'''
												a_						:						Tuple 							= f'''output_blocks.{current_layer-1}.1'''
												a_						:						Tuple 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__)
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1):
												a_						:						Optional[int] 							= f'''up_blocks.{i}.resnets.{j}'''
												a_						:						List[Any] 							= f'''output_blocks.{current_layer}.0'''
												a_						:						Any 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__  ,	has_skip=a__)
												a_						:						Any 							= f'''up_blocks.{i}.attentions.{j}'''
												a_						:						List[str] 							= f'''output_blocks.{current_layer}.1'''
												a_						:						List[str] 							= convert_attention(
												    a__  ,	a__  ,	a__  ,	a__  ,	a__)
												current_layer += 1

									if i != len(a__) - 1:
												a_						:						str 							= f'''up_blocks.{i}.upsamplers.0'''
												a_						:						Optional[int] 							= f'''output_blocks.{current_layer-1}.2'''
												a_						:						Union[str, Any] 							= convert_resnet(a__  ,	a__  ,	a__  ,	a__)

			a_						:						Tuple 							= checkpoint["""out.0.weight"""]
			a_						:						int 							= checkpoint["""out.0.bias"""]
			a_						:						str 							= checkpoint["""out.2.weight"""]
			a_						:						List[Any] 							= checkpoint["""out.2.bias"""]

			return new_checkpoint


if __name__ == "__main__":
	__snake_case				:      List[Any]   		=			argparse.ArgumentParser()

	parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
	parser.add_argument(
	    """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
	)
	parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

	__snake_case				:      List[str]   		=			parser.parse_args()
	__snake_case				:      List[Any]   		=			strabool(args.class_cond)

	__snake_case				:      List[Any]   		=			os.path.basename(args.unet_path)
	print(F"""Checkpoint: {ckpt_name}""")

	# Get U-Net config
	if "imagenet64" in ckpt_name:
		__snake_case				:      str   		=			IMAGENET_64_UNET_CONFIG
	elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
		__snake_case				:      Union[str, Any]   		=			LSUN_256_UNET_CONFIG
	elif "test" in ckpt_name:
		__snake_case				:      List[str]   		=			TEST_UNET_CONFIG
	else:
		raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

	if not args.class_cond:
		__snake_case				:      List[Any]   		=			None

	__snake_case				:      str   		=			con_pt_to_diffuser(args.unet_path, unet_config)

	__snake_case				:      Any   		=			UNetaDModel(**unet_config)
	image_unet.load_state_dict(converted_unet_ckpt)

	# Get scheduler config
	if "cd" in ckpt_name or "test" in ckpt_name:
		__snake_case				:      List[str]   		=			CD_SCHEDULER_CONFIG
	elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
		__snake_case				:      str   		=			CT_IMAGENET_64_SCHEDULER_CONFIG
	elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
		__snake_case				:      Optional[int]   		=			CT_LSUN_256_SCHEDULER_CONFIG
	else:
		raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

	__snake_case				:      Tuple   		=			CMStochasticIterativeScheduler(**scheduler_config)

	__snake_case				:      Tuple   		=			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
	consistency_model.save_pretrained(args.dump_path)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


